FlinkSQL ChangeLog

01 Changelog相关优化规则

0101 运行upsert-kafka作业

登录sql-client，创建一个upsert-kafka的sql作业（注意，这里发送给kafka的消息必须带key，普通只有value的消息无法解析，这里的key即是主键的值）

CREATE TABLE pageviews_per_region (user_region STRING,pv STRING,PRIMARY KEY (user_region) NOT ENFORCED  -- 设置主键
) WITH ('connector' = 'upsert-kafka','topic' = 'pageviews_per_region','properties.bootstrap.servers' = 'xxxxxx:9092','key.format' = 'csv','value.format' = 'csv'
);select * from pageviews_per_region;

发送消息带key和消费消息显示key方式如下

kafka-console-producer.sh --broker-list xxxxxx:9092 --topic pageviews_per_region --property "parse.key=true" --property "key.separator=:"
key1:value1,value1
key2:value2,value2kafka-console-consumer.sh --bootstrap-server xxxxxx:9092 --topic pageviews_per_region --from-beginning --property print.key=true

作业的DAG图如下
在这里插入图片描述

0102 StreamPhysicalChangelogNormalize

DAG图中有一个ChangelogNormalize，代码中搜索到对应的类是StreamPhysicalChangelogNormalize，这是一个对changelog数据做规范化的类，注释如下

/*** Stream physical RelNode which normalizes a changelog stream which maybe an upsert stream or a* changelog stream containing duplicate events. This node normalize such stream into a regular* changelog stream that contains INSERT/UPDATE_BEFORE/UPDATE_AFTER/DELETE records without* duplication.*/
class StreamPhysicalChangelogNormalize(

功能就是转成对应的exec节点

override def translateToExecNode(): ExecNode[_] = {val generateUpdateBefore = ChangelogPlanUtils.generateUpdateBefore(this)new StreamExecChangelogNormalize(unwrapTableConfig(this),uniqueKeys,generateUpdateBefore,InputProperty.DEFAULT,FlinkTypeFactory.toLogicalRowType(getRowType),getRelDetailedDescription)
}

0103 StreamPhysicalTableSourceScanRule

StreamPhysicalChangelogNormalize是在优化规则StreamPhysicalTableSourceScanRule当中创建的，如下流式的FlinkLogicalTableSourceScan会应用该规则

class StreamPhysicalTableSourceScanRuleextends ConverterRule(classOf[FlinkLogicalTableSourceScan],FlinkConventions.LOGICAL,FlinkConventions.STREAM_PHYSICAL,"StreamPhysicalTableSourceScanRule") {

创建StreamPhysicalChangelogNormalize，也就是转为changelog的条件如下

if (isUpsertSource(resolvedSchema, table.tableSource) ||isSourceChangeEventsDuplicate(resolvedSchema, table.tableSource, config)
) {

isUpsertSource判断是否为upsert流，判断逻辑如下

public static boolean isUpsertSource(ResolvedSchema resolvedSchema, DynamicTableSource tableSource) {if (!(tableSource instanceof ScanTableSource)) {return false;}ChangelogMode mode = ((ScanTableSource) tableSource).getChangelogMode();boolean isUpsertMode =mode.contains(RowKind.UPDATE_AFTER) && !mode.contains(RowKind.UPDATE_BEFORE);boolean hasPrimaryKey = resolvedSchema.getPrimaryKey().isPresent();return isUpsertMode && hasPrimaryKey;
}

其中ChangelogMode在各自数据源实现类的getChangelogMode接口中定义，如JDBC只支持insert

@Override
public ChangelogMode getChangelogMode() {return ChangelogMode.insertOnly();
}

isSourceChangeEventsDuplicate判断不是upsert的更改流，判断逻辑如下

public static boolean isSourceChangeEventsDuplicate(ResolvedSchema resolvedSchema,DynamicTableSource tableSource,TableConfig tableConfig) {if (!(tableSource instanceof ScanTableSource)) {return false;}ChangelogMode mode = ((ScanTableSource) tableSource).getChangelogMode();boolean isCDCSource =!mode.containsOnly(RowKind.INSERT) && !isUpsertSource(resolvedSchema, tableSource);boolean changeEventsDuplicate =tableConfig.get(ExecutionConfigOptions.TABLE_EXEC_SOURCE_CDC_EVENTS_DUPLICATE);boolean hasPrimaryKey = resolvedSchema.getPrimaryKey().isPresent();return isCDCSource && changeEventsDuplicate && hasPrimaryKey;
}

综合来说要走StreamPhysicalChangelogNormalize这一条调用链，就不能是insertOnly的数据源，但目前大部分Flink实现的数据源包括Iceberg都是insertOnly的

0104 更新模式

Flink相关的更新模式类有如下几个：RowKind、ChangelogMode、UpdateKind

RowKind

RowKind是定义更新流每条数据的类型，其中对于更新有；两条数据，一条删除旧数据，一条插入新数据

/** Insertion operation. */
INSERT("+I", (byte) 0),/*** Update operation with the previous content of the updated row.** <p>This kind SHOULD occur together with {@link #UPDATE_AFTER} for modelling an update that* needs to retract the previous row first. It is useful in cases of a non-idempotent update,* i.e., an update of a row that is not uniquely identifiable by a key.*/
UPDATE_BEFORE("-U", (byte) 1),/*** Update operation with new content of the updated row.** <p>This kind CAN occur together with {@link #UPDATE_BEFORE} for modelling an update that* needs to retract the previous row first. OR it describes an idempotent update, i.e., an* update of a row that is uniquely identifiable by a key.*/
UPDATE_AFTER("+U", (byte) 2),/** Deletion operation. */
DELETE("-D", (byte) 3);

ChangelogMode

ChangelogMode定义数据源的更新模式，主要三种，就是包含不同的RowKind的类型

private static final ChangelogMode INSERT_ONLY =ChangelogMode.newBuilder().addContainedKind(RowKind.INSERT).build();private static final ChangelogMode UPSERT =ChangelogMode.newBuilder().addContainedKind(RowKind.INSERT).addContainedKind(RowKind.UPDATE_AFTER).addContainedKind(RowKind.DELETE).build();private static final ChangelogMode ALL =ChangelogMode.newBuilder().addContainedKind(RowKind.INSERT).addContainedKind(RowKind.UPDATE_BEFORE).addContainedKind(RowKind.UPDATE_AFTER).addContainedKind(RowKind.DELETE).build();

UpdateKind

UpdateKind是针对update这种更新类型细分

/** NONE doesn't represent any kind of update operation. */
NONE,/*** This kind indicates that operators should emit update changes just as a row of {@code* RowKind#UPDATE_AFTER}.*/
ONLY_UPDATE_AFTER,/*** This kind indicates that operators should emit update changes in the way that a row of {@code* RowKind#UPDATE_BEFORE} and a row of {@code RowKind#UPDATE_AFTER} together.*/
BEFORE_AND_AFTER

02 StreamExecChangelogNormalize

StreamExecChangelogNormalize的处理流程中根据是否启用table.exec.mini-batch.enabled分为微批处理和单数据的流处理

微批处理使用ProcTimeMiniBatchDeduplicateKeepLastRowFunction，流式使用ProcTimeDeduplicateKeepLastRowFunction，两者的核心差别就是微批会缓存数据使用一个for循环处理

这两个函数除了StreamPhysicalChangelogNormalize这一条链路外，还有StreamExecDeduplicate这一条链路，对应StreamPhysicalRankRule，是一个排序的东西

for (Map.Entry<RowData, RowData> entry : buffer.entrySet()) {RowData currentKey = entry.getKey();RowData currentRow = entry.getValue();ctx.setCurrentKey(currentKey);if (inputInsertOnly) {processLastRowOnProcTime(currentRow,generateUpdateBefore,generateInsert,state,out,isStateTtlEnabled,equaliser);} else {processLastRowOnChangelog(currentRow, generateUpdateBefore, state, out, isStateTtlEnabled, equaliser);}
}

processLastRowOnProcTime

对数据按照时间语义进行去重，将当前数据作为最新，这个函数只针对insert only的数据

static void checkInsertOnly(RowData currentRow) {Preconditions.checkArgument(currentRow.getRowKind() == RowKind.INSERT);
}

整套处理逻辑就是对数据根据场景修改数据的RowKind类型

} else {if (generateUpdateBefore) {preRow.setRowKind(RowKind.UPDATE_BEFORE);out.collect(preRow);}currentRow.setRowKind(RowKind.UPDATE_AFTER);out.collect(currentRow);
}

processLastRowOnChangelog

这个函数就是按Key去重，本质上也是针对数据修改RowKind

核心的一块功能就是更新的时候要将前一个数据修改为UPDATE_BEFORE

} else {if (generateUpdateBefore) {preRow.setRowKind(RowKind.UPDATE_BEFORE);out.collect(preRow);}currentRow.setRowKind(RowKind.UPDATE_AFTER);out.collect(currentRow);
}

函数整体借用的是Flink的state功能，从状态中获取前面的数据，所以对状态缓存由要求；另外针对非删除型的数据，如果TTL没有开的话，就不会更新前面的数据

if (!isStateTtlEnabled && equaliser.equals(preRow, currentRow)) {// currentRow is the same as preRow and state cleaning is not enabled.// We do not emit retraction and update message.// If state cleaning is enabled, we have to emit messages to prevent too early// state eviction of downstream operators.return;
}

03 初始RowKind来源

前面的流程里，在进行changelog转换的时候，数据是已经存在一个RowKind的值了，这一章追踪初始RowKind的来源

基于Flink-27的设计，Kafka数据源处理任务有一个KafkaRecordEmitter，emitRecord当中做数据的反序列化

deserializationSchema.deserialize(consumerRecord, sourceOutputWrapper);

最终走到DeserializationSchema.deserialize完成最终的反序列化

default void deserialize(byte[] message, Collector<T> out) throws IOException {T deserialize = deserialize(message);if (deserialize != null) {out.collect(deserialize);}
}

这里message是一个二进制数组，实际是Kafka的数据类型ConsumerRecord。根据SQL当中的配置，value反序列化使用的是csv，所以走到CsvRowDataDeserializationSchema当中处理

final JsonNode root = objectReader.readValue(message);
return (RowData) runtimeConverter.convert(root);

这里读出来的root是数据的key，convert的转化的实现类是CsvToRowDataConverters，其createRowConverter接口当中创建了转化函数，函数中将数据转化为了Flink的数据类型GenericRowData

GenericRowData row = new GenericRowData(arity);

GenericRowData的定义当中，有初始化RowKind，就是insert

public GenericRowData(int arity) {this.fields = new Object[arity];this.kind = RowKind.INSERT; // INSERT as default
}

04 补充

0401 delete

按照官方说法，发送一个空消息就会产生delete

 Also, null values are interpreted in a special way: a record with a null value represents a “DELETE”.

使用kafka producer控制台发送空消息无法解析

[ERROR] Could not execute SQL statement. Reason:
org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.exc.MismatchedInputException: No content to map due to end-of-inputat [Source: UNKNOWN; byte offset: #UNKNOWN]

官方说法是kafka的控制台版本对 null的支持问题，需要3.2以上版本
https://issues.apache.org/jira/browse/FLINK-27663?jql=project%20%3D%20FLINK%20AND%20text%20~%20%22upsert-kafka%22

空值处理逻辑在DynamicKafkaDeserializationSchema.deserialize当中
这里根据输入的数据是否空值进行分支处理；非空值时走的就是前三章的逻辑，也就是这里是前三章逻辑的入口

if (record.value() == null && upsertMode) {// collect tombstone messages in upsert mode by handoutputCollector.collect(null);
} else {valueDeserialization.deserialize(record.value(), outputCollector);
}

空值时走到OutputProjectionCollector.emitRow，这里会设置初始类型为DELETE

if (physicalValueRow == null) {if (upsertMode) {rowKind = RowKind.DELETE;} else {throw new DeserializationException("Invalid null value received in non-upsert mode. Could not to set row kind for output record.");}
} else {rowKind = physicalValueRow.getRowKind();
}